Method and system having hypothesis type variable thresholds

ABSTRACT

A method (and system) for spoken dialog confirmation classifies a plurality of spoken dialog hypotheses, and assigns a threshold to each class of spoken dialog hypotheses.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention generally relates to a method and system foraccepting or rejecting a spoken dialog hypothesis. More particularly,the present invention relates to a method and system for determiningacceptance of a spoken dialog hypothesis by comparing a confidence levelto a threshold that is based upon the type of hypothesis.

2. Description of the Related Art

Spoken dialog systems need to determine whether to accept or reject ahypothesis. For example, a spoken dialog system may receive a soundsignal representing the voice of a user of the system and the system maythen compare the input signal to potential candidates. The system woulddetermine a hypothesis by, for example, selecting a candidate from alist of candidates that has the highest confidence indicator. However,these systems need to determine whether the confidence indicator is highenough such that the system should accept the hypothesis as beingcorrect. It is undesirable to automatically accept a hypothesis merelybecause it has the highest confidence of all potential candidatesbecause the candidate with the highest confidence level may have arelatively low confidence level. These conventional systems address thisproblem by comparing the confidence level of a hypothesis to apredetermined threshold. If the confidence level exceeds thepredetermined threshold, then the hypothesis is accepted. If theconfidence level does not exceed the predetermined threshold, then thehypothesis is rejected. In this manner, conventional systems attempt toavoid making wrong decisions.

Some conventional systems attempt to further reduce the chance of anincorrect acceptance by providing two different thresholds. Thesesystems incorporate a rejection threshold and a confirmation threshold.The rejection threshold is always lower than the confirmation threshold.Any hypothesis having a confidence level below the rejection thresholdis rejected. Any hypothesis having a confidence level above therejection threshold, but below the confirmation threshold requires thatthe user confirm whether the hypothesis is correct before the hypothesisis accepted by the system. Any hypothesis having a confidence levelabove the confirmation threshold is accepted without confirmation.

One conventional system derives features about a hypothesis from arecognizer and a parser and then classifies these features to determinewhether to accept or reject the hypothesis. This system is concernedmore with efficient operation rather than achieving the correct result.

Another conventional system adjusts acceptance thresholds based upon thelength of the input audio. Since this system only considers the durationof the input audio, the system does not rely upon any hypothesizedinformation at all to make a threshold determination.

SUMMARY OF THE INVENTION

In view of the foregoing and other exemplary problems, drawbacks, anddisadvantages of the conventional methods and structures, an exemplaryfeature of the present invention is to provide a method and structure inwhich a spoken dialog system classifies a plurality of hypotheses andassigns a threshold to each class of hypothesis.

In a first exemplary aspect of the present invention, a method forspoken dialog confirmation includes classifying a plurality of spokendialog hypotheses into a plurality of classes of spoken dialoghypotheses, and assigning a threshold to each of the classes of spokendialog hypotheses.

In a second exemplary aspect of the present invention, a system forspoken dialog confirmation includes a spoken dialog hypothesisclassifier that classifies a plurality of spoken dialog hypotheses intoa plurality of classes of spoken dialog hypotheses, and a thresholdassigner that assigns a threshold to each of the classes of spokendialog hypotheses.

In a third exemplary aspect of the present invention, a system forspoken dialog confirmation includes means for classifying a plurality ofspoken dialog hypotheses into a plurality of classes of spoken dialoghypotheses, and means for assigning a threshold to each of the classesof spoken dialog hypotheses.

An exemplary embodiment of the present invention uses differentrejection and confirmation thresholds for different user inputs bymaking these thresholds a function of user utterance and semanticinterpretation.

An exemplary embodiment of the present invention determines thresholdsbased upon a hypothesis typing at a word level. This word levelhypothesis and semantic interpretation makes it quite easy to implementinto existing dialog systems.

An exemplary embodiment of the present invention includes a thresholdthat is based upon a type or class of hypothesis. A type of hypothesisis specified by a grouping of hypotheses. Given a set of hypotheses, anyarbitrary partition of that set defines a set of types, where eachcluster in that partition indicates a type.

An exemplary embodiment of the invention identifies an hypothesis withinan input signal; and identifies a threshold based upon the typehypothesis

A hypothesis is, in general, the set of pieces of information (includingmultiple word level transcriptions, their semantic interpretations,associated scores, and the like) that are generated when a recognitionprocess is carried out on audio. In an exemplary embodiment of thepresent invention, only those results which are derived using semanticinterpretation are used.

In an exemplary embodiment of the present invention, a jointoptimization of clusters and thresholds is carried out and there aremany possible ways in which this joint optimization may be approximatedand achieved. An example of joint optimization is an iterative processthat starts with some clustering of hypotheses, finds optimal thresholdsfor that clustering, then given those thresholds for clusters,determines if changing cluster membership of some hypotheses results inimproved performance. Then, given this new clustering, finds optimalthresholds, and so forth. This process attempts to learn both theoptimal clustering and optimal thresholds for the optimal clustering.

In an exemplary embodiment of the present invention, hypotheses may betyped based upon historically recorded frequencies. For example, anhypothesis having a higher frequency of occurrence in the past is morelikely to occur again than another hypothesis having a lower frequencyof occurrence. Thus, various hypotheses may be typed (classified) basedupon these frequencies and the thresholds adjusted based upon the typing(classification).

Another exemplary embodiment may determine the type of a hypothesisbased upon historical acceptance rate. For example, a system maycontinuously record the number of false accepts or false rejects for aset of hypotheses and group (type) these hypotheses based upon thehistorical data. Those hypotheses having a higher false acceptance ratemay be provided with a higher acceptance threshold than those hypotheseshaving a lower false acceptance rate.

In an exemplary embodiment of the present invention, the hypothesis aregrouped based upon an historical frequency of occurrence. For example,those hypotheses having a high historical frequency may be provided witha lower threshold, while those hypotheses having a lower historicalfrequency may be provided with a higher threshold.

In another exemplary embodiment of the present invention, eachindividual hypothesis may have a threshold assigned to it individually.

In yet another exemplary embodiment of the present invention, thehypotheses may be grouped based upon a difficulty of recognition. Forexample, those hypotheses having a low difficulty of recognition may beprovided with a lower threshold, while those hypotheses having a higherdifficulty of recognition may be provided with a higher threshold.

These and many other advantages may be achieved with the presentinvention.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other exemplary purposes, aspects and advantages willbe better understood from the following detailed description of anexemplary embodiment of the invention with reference to the drawings, inwhich:

FIG. 1 illustrates an exemplary spoken dialog confirmation system 100 inaccordance with the present invention;

FIG. 2 is a flowchart 200 of an exemplary method of spoken dialogconfirmation in accordance with the present invention;

FIG. 3 illustrates a typical hardware configuration 300 which may beused for implementing the inventive system and method for spoken dialogconfirmation; and

FIG. 4 illustrates exemplary signal bearing media 400 for storinginstructions for a spoken dialog system in accordance with the presentinvention.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS OF THE INVENTION

Referring now to the drawings, and more particularly to FIGS. 1-4, thereare shown exemplary embodiments of the method and structures of thepresent invention.

FIG. 1 illustrates an exemplary spoken dialog confirmation system 100 inaccordance with the present invention. The spoken dialog confirmationsystem 100 includes a hypothesis classifier 102, a threshold assigner104, a storage 106, an hypothesis generator 108, a threshold determiner110, and a comparator 112. The hypothesis classifier 102 classifies aplurality of hypotheses that are stored in the storage 106. For example,the classifier 102 may classify the hypotheses according to thefrequency of occurrence.

Based upon the classification assigned by the hypothesis classifier 102,the threshold assigner 104 assigns a threshold to each hypothesis andthen stores the corresponding thresholds in the storage 106.

The hypothesis generator 108 receives an input utterance (i.e. a spokendialog) and generates a hypothesis based upon the input utterance. Themethod for determining a hypothesis may be performed according tostandard semantic interpretation methods. These methods also provide aconfidence indicator that indicates the degree of confidence with whichthe system believes the hypothesis is likely to correspond correctly tothe input utterance.

The threshold determiner 110 receives the generated hypothesis alongwith the confidence indicator and determines a corresponding thresholdvalue in accordance with the previously assigned thresholds stored inthe storage 106 as assigned by the threshold assigner 104. The thresholddeterminer 110 assigns the corresponding threshold to the generatedhypothesis.

The comparator 112 compares the confidence indicator value against thethreshold value that is provided by the threshold determiner 110. If theconfidence indicator exceeds the threshold, then the comparator 112outputs the hypothesis as an accepted hypothesis.

FIG. 2 is a flowchart 200 of an exemplary method for spoken dialogconfirmation in accordance with the present invention. The flowchart 200starts at step 202 and continues to step 204. In step 204, the methodreceives a set of hypotheses and continues to step 206. In step 206, themethod classifies the hypotheses according to, for example, thefrequency of occurrence. Next, the method assigns a threshold to eachclassification of hypotheses and continues to step 210.

In step 210, the method receives an utterance as input and continues tostep 212. In step 212, the method correlates the utterance to anhypothesis and provides a confidence indicator for the hypothesis. Instep 214, the method correlates a threshold to the hypothesis accordingto the classification of the hypothesis in step 206. Next, the methodcompares the confidence indicator to the threshold and, if theconfidence indicator exceeds the threshold, outputs the hypothesis as anaccepted hypothesis. The method ends at step 218.

The inventors compared an exemplary embodiment of the present inventionto two conventional systems which use different dialog states—one thatuses a rejection and a confirmation threshold, and one that only usesrejection threshold. The experiments were conducted by the inventorsusing data that was collected from usage of these states by real callers(results presented below). The experimental results show that thepresent invention achieves significant reductions in unnecessaryconfirmations (confirming when recognized target is correct) and infalse accepts and false rejects.

The first dialog state, called “Main-Menu,” provides an open-endedprompt and uses a statistical language model for recognition and astatistical classer for finding semantic interpretation. The baseline(current) implementation of this state uses two hypothesis independentthresholds; one for rejection and one for confirmation. These thresholdvalues are shown in Table 1. Using these thresholds, the rejection orconfirmation is determined as follows: If the actual confidence score isless than the rejection threshold, then the hypothesis is rejected. Ifthe confidence score is equal to or above the rejection threshold butequal to or below the confirmation threshold, then the hypothesis isconfirmed, and if the confidence is above the confirmation threshold,then the hypothesis is accepted. If the confirmation threshold value issmaller then a rejection threshold for a target, then that hypothesis isrejected.

Table 1 illustrates thresholds of a conventional spoken dialog system.The rejection threshold is the same for all possible hypothesis. Theconfirmation threshold is also the same for all hypothesis. However, theconfirmation threshold is only applied to a subset of hypotheses.

TABLE 1 Rejection Confirmation Hypothesis threshold thresholdHandsetRelated_General 30 None Universals_WAMC 30 NoneUniversals_Operator 30 None Universals_Goodbye 30 NoneAccountRelated_MinutesUsed 30 51 ServiceProblems_DroppedCalls 30 51PayNow_General 30 51 _rest_(—) 30 51

The inventors applied this conventional system to a data set andcollected results provided by the conventional system. The results foundthat 95.7% of the hypotheses were recognizable as being within thevocabulary of the system (i.e. being within the grammar). For these, “ingrammar” hypotheses, the conventional system provided a correctacceptance rate of 77.3%, a false acceptance rate of 2.6%, a falseconfirmation rate of 12.3%, and a false rejection rate of 3.5%. Theconventional system also had 4.3% outside of the vocabulary (i.e.out-of-grammar) that had a correct reject rate of 2.8%, a 0.7% falseacceptance rate, and a 0.8% false confirmation rate. Thus, theconventional system had an overall failure rate of 19.9%.

The specific false rejection/confirmation/acceptance numbers on some ofthese hypotheses are illustrated in Table 2 below:

TABLE 2 False False Spoken Correct ac- con- False Hypothesis (count)accept cept firm reject AccountRelated_MinutesUsed 641 350 12 227 52PayNow_General 536 338 31 150 17 ServiceProblems_DroppedCalls 416 315 1167 23

The inventors applied an exemplary embodiment of the present inventionhaving a constraint optimization (on some training data separate fromthe test data) to keep the total false-accepts to below 3.45% and totalrejects (correct rejects+false rejects) below 7.5%. Constraintoptimization is carried out by placing constraints on maximumpermissible false accepts, false rejects, and false confirms, minimumpermissible correct accepts and correct rejects, or some combination ofthese. The optimization process attempts to minimize the total falseaccepts, confirms, and rejects while making sure that the constraintsare not violated.

The inventors obtained the following results using the followingthresholds in accordance with an exemplary embodiment of the presentinvention:

TABLE 3 Rejection Confirmation Hypothesis threshold thresholdHandsetRelated_General 38 None Universals_WAMC 28 NoneUniversals_Operator 28 None Universals_Goodbye 28 NoneAccountRelated_MinutesUsed 30 36 ServiceProblems_DroppedCalls 30 46PayNow_General 30 45 _rest_(—) 30 51

Note that the rejection thresholds and the confirmation thresholdschange depending upon the hypothesis. With these rejection andconfirmation threshold values, the exemplary embodiment obtained thefollowing results. The in-grammar percentage rate was 95.7%, the correctacceptance rate was 81.4%, the false acceptance rate was 2.6%, the falseconfirmation rate was 8.2%, and the false rejection rate was 3.5%. Theout-of-grammar rate was 4.3%, the correct reject rate was 2.9%, thefalse acceptance rate 0.7%, and the false confirmation rate was 0.7%.Thus, the overall failure rate was 15.7%. This is a significantimprovement over the 19.9% failure rate of the conventional system andmethod.

Comparing with the baseline numbers, there is a large drop in totalfalse acceptance/confirmation/rejection. This drop is largely due todrop in false confirmation—which means that the recognizer had correctlyrecognized the target but it was still getting confirmed due to poorthreshold settings.

With these new values, the false accept/confirm/reject numbers on someof these hypotheses are illustrated in Table 4 below:

TABLE 4 False False Spoken Correct ac- con- False Hypothesis (count)accept cept firm reject AccountRelated_MinutesUsed 641 547 12 30 52PayNow_General 536 399 32 32 17 ServiceProblems_DroppedCalls 416 336 1046 24

Comparing the numbers in Table 4 with those of Table 2, with new,hypothesis type dependent thresholds, a large drop in falseconfirmations is achieved and the correct accept rate is much higher.Furthermore, these improvements come without an impact on false-acceptsand rejects.

The inventors also examined the application of a conventional system andan exemplary embodiment of the present invention upon test data calledBackOffMainMenu. This application does not carry out confirmation andhence has only one rejection threshold in the baseline (current) system.Table 5 illustrates the conventional system having the same rejectionthreshold regardless of the type of Hypothesis.

TABLE 5 Rejection Hypothesis threshold Help_me_with_something_else 39Operator 39 My_bill, My_plan 39 _rest_(—) 39

With these threshold settings, using the conventional system resulted inan in-grammar rate of 79.9% with a correct acceptance rate of 71.2%, afalse acceptance rate of 2.1%, and a false rejection rate of 6.6%. Forthe out-of-grammar rate of 20.1%, the result was a correct reject rateof 11.3%, and a false acceptance rate of 8.9%. This results in anoverall failure rate of 17.6%.

The false accept/reject rates on some frequent targets are illustratedin Table 6 below:

TABLE 6 Spoken Correct False False Hypothesis (count) accept acceptreject Help_me_with_something_else 915 737 45 133 Operator 686 674 1 11My_bill 331 308 2 21 My_plan 295 270 9 16

After constraint optimization, an exemplary embodiment of the presentinvention having the rejection thresholds for different hypotheses wasapplied to the test data. For example, the rejection thresholds areillustrated by Table 7 below:

TABLE 7 Hypothesis Rejection threshold Help_me_with_something_else 29Operator 39 My_bill, My_plan 39 _rest_(—) 46

The exemplary embodiment of the present invention provided an in-grammarrate of 79.9% having a correct acceptance rate of 73.4%, a falseacceptance rate of 1.9%, and a false rejection rate of 4.6%, anout-of-grammar rate of 20.1% having a correct rejection rate of 12.5%,and a false acceptance rate of 7.7%. This results in an overall failurerate of 14.2%, which is significantly lower than the 17.6% failure rateof the conventional system and method.

Exemplary false accept/rejects on some frequent hypotheses in the testdata applied to the exemplary embodiment are illustrated in Table 8below:

TABLE 8 Spoken Correct False False Hypothesis (count) accept acceptreject Help_me_with_something_else 915 818 40 57 Operator 686 674 0 12My_bill 331 308 2 21 My_plan 295 270 8 17

Similar to the Main-Menu results described earlier, for this state alsothere is a large overall improvement. There is a large gain in thecorrect accepts of the most frequent hypothesis and practically nochange for others.

Referring now to FIG. 3, system 300 illustrates a typical hardwareconfiguration which may be used for implementing the inventive systemand method for spoken dialog confirmation. The configuration haspreferably at least one processor or central processing unit (CPU) 310.The CPUs 302 are interconnected via a system bus 312 to a random accessmemory (RAM) 314, read-only memory (ROM) 316, input/output (I/O) adapter318 (for connecting peripheral devices such as disk units 321 and tapedrives 340 to the bus 312), user interface adapter 322 (for connecting akeyboard 324, mouse 326, speaker 328, microphone 332, and/or other userinterface device to the bus 312), a communication adapter 334 forconnecting an information handling system to a data processing network,the Internet, and Intranet, a personal area network (PAN), etc., and adisplay adapter 336 for connecting the bus 312 to a display device 338and/or printer 339. Further, an automated reader/scanner 341 may beincluded. Such readers/scanners are commercially available from manysources.

In addition to the system described above, a different aspect of theinvention includes a computer-implemented method for performing theabove method. As an example, this method may be implemented in theparticular environment discussed above.

Such a method may be implemented, for example, by operating a computer,as embodied by a digital data processing apparatus, to execute asequence of machine-readable instructions. These instructions may residein various types of signal-bearing media.

Thus, this aspect of the present invention is directed to a programmedproduct, including signal-bearing media tangibly embodying a program ofmachine-readable instructions executable by a digital data processor toperform the above method.

Such a method may be implemented, for example, by operating the CPU 310to execute a sequence of machine-readable instructions. Theseinstructions may reside in various types of signal bearing media.

Thus, this aspect of the present invention is directed to a programmedproduct, comprising signal-bearing media tangibly embodying a program ofmachine-readable instructions executable by a digital data processorincorporating the CPU 310 and hardware above, to perform the method ofthe invention.

This signal-bearing media may include, for example, a RAM containedwithin the CPU 310, as represented by the fast-access storage forexample. Alternatively, the instructions may be contained in anothersignal-bearing media, such as a magnetic data storage diskette 400 orCD-ROM 402, (FIG. 4), directly or indirectly accessible by the CPU 310.

Whether contained in the computer server/CPU 310, or elsewhere, theinstructions may be stored on a variety of machine-readable data storagemedia, such as DASD storage (e.g., a conventional “hard drive” or a RAIDarray), magnetic tape, electronic read-only memory (e.g., ROM, EPROM, orEEPROM), an optical storage device (e.g., CD-ROM, WORM, DVD, digitaloptical tape, etc.), paper “punch” cards, or other suitablesignal-bearing media. In an illustrative embodiment of the invention,the machine-readable instructions may comprise software object code,complied from a language such as “C,” etc.

While the invention has been described in terms of several exemplaryembodiments, those skilled in the art will recognize that the inventioncan be practiced with modification.

Further, it is noted that, Applicants' intent is to encompassequivalents of all claim elements, even if amended later duringprosecution.

What is claimed is:
 1. A method of configuring a system to performspoken dialog confirmation, comprising: operating at least oneprogrammed processor to perform: classifying a plurality of spokendialog hypotheses into a plurality of classes of spoken dialoghypotheses, each of the plurality of spoken dialog hypotheses being apotential semantic interpretation that a semantic interpreter is able tooutput as a result of a semantic interpretation process performed on aninput, the plurality of classes comprising a first class of spokendialog hypotheses comprising at least one first spoken dialog hypothesisand a second class of spoken dialog hypotheses comprising at least onesecond spoken dialog hypothesis, the at least one first spoken dialoghypothesis being different from the at least one second spoken dialoghypothesis; and determining, for each of said plurality of classes ofspoken dialog hypotheses, a threshold for use in evaluating a confidencelevel of the semantic interpreter in an output of the semanticinterpreter that comprises one of the one or more hypotheses in theclass, wherein the determining comprises determining a first thresholdfor the first class and a second threshold for the second class, thefirst threshold having a different value than the second threshold,wherein the determining comprises adjusting the first threshold based atleast in part on a number of times the at least one first spoken dialoghypothesis was previously output by the semantic interpreter.
 2. Themethod of claim 1, further comprising operating the at least oneprogrammed processor to perform acts of: receiving an utterance as aninput; generating a spoken dialog hypothesis and a confidence value forsaid utterance; determining a corresponding threshold for said spokendialog hypothesis based upon said classifying; and comparing saidconfidence value of said spoken dialog hypothesis to each threshold. 3.The method of claim 2, further comprising operating the at least oneprogrammed processor to perform accepting said spoken dialog hypothesisif said confidence value exceeds said threshold.
 4. The method of claim2, further comprising operating the at least one programmed processor toperform rejecting said spoken dialog hypothesis if said confidence valuedoes not exceed said threshold.
 5. The method of claim 1, wherein saiddetermining a threshold to each class comprises determining aconfirmation threshold and a rejection threshold to each class, andwherein the method further comprises operating the at least oneprogrammed processor to recognize an utterance spoken by a user by:receiving the utterance as an input; generating a spoken dialoghypothesis and a confidence value for said utterance; determiningcorresponding rejection and confirmation thresholds for said spokendialog hypothesis based upon said classifying; comparing said confidencevalue of said spoken dialog hypothesis to said thresholds; acceptingsaid spoken dialog hypothesis if said confidence value exceeds saidconfirmation threshold; confirming said spoken dialog hypothesis if saidconfidence value does not exceed said confirmation threshold but exceedssaid rejection threshold; and rejecting said spoken dialog hypothesis ifsaid confidence value does not exceed said rejection threshold and saidspoken dialog hypothesis is not confirmed.
 6. The method of claim 1,wherein said spoken dialog hypotheses are classified according to adifficulty of recognition.
 7. A non-transitory machine-readable storagemedium having encoded thereon machine-executable instructions that, whenexecuted by a digital processing unit, causes the digital processingunit to perform the method of claim
 1. 8. The method of claim 1, furthercomprising operating a speech recognition system to carry out a speechinteraction with a user, wherein operating the speech recognition systemcomprises operating the at least one processor to perform the semanticinterpreting, the determining, and the comparing.
 9. The method of claim1, wherein adjusting the first threshold based at least in part on thenumber of times the at least one first spoken dialog hypothesis waspreviously output by the semantic interpreter comprises applying anoptimization algorithm to adjust the first threshold based at least inpart on one or more indications of whether, when one of the at least onefirst spoken dialog hypothesis was output in each of the number oftimes, it was determined that the one of the at least one first spokendialog hypothesis was a correct interpretation of input to the semanticinterpreter.
 10. The method of claim 1, wherein adjusting the firstthreshold based at least in part on a number of times the at least onefirst spoken dialog hypothesis was previously output by the semanticinterpreter comprises adjusting the first threshold in indirectproportion to the number of times the at least one first spoken dialoghypothesis was previously output by the semantic interpreter.
 11. Themethod of claim 10, wherein adjusting the first threshold in proportionto the number of times the at least one first spoken dialog hypothesiswas previously output by the semantic interpreter comprises adjustingthe first threshold in indirect proportion to the number of times the atleast one first spoken dialog hypothesis was, when previously output,determined to be a correct interpretation of input to the semanticinterpreter.
 12. A system for spoken dialog confirmation, comprising: atleast one processor programmed to act as: a spoken dialog hypothesisclassifier that classifies a plurality of hypotheses into a plurality ofclasses of spoken dialog hypotheses, each of the plurality of spokendialog hypotheses being a potential semantic interpretation that asemantic interpreter is able to output as a result of a semanticinterpretation process performed on an input, the plurality of classescomprising a first class of spoken dialog hypotheses comprising at leastone first spoken dialog hypothesis and a second class of spoken dialoghypotheses comprising at least one second spoken dialog hypothesis, theat least one first spoken dialog hypothesis being different from the atleast one second spoken dialog hypothesis; and a threshold assigner thatdetermines, for each of said plurality of classes of spoken dialoghypotheses, a threshold for use in evaluating a confidence level of thesemantic interpreter in an output of the semantic interpreter thatcomprises one of the one or more hypotheses in the class, wherein thethreshold assigner determines a first threshold for the first class anda second threshold for the second class, the first threshold having adifferent value than the second threshold, wherein the determiningcomprises adjusting the first threshold based at least in part on anumber of times the at least one first spoken dialog hypothesis waspreviously output by the semantic interpreter.
 13. The system of claim12, wherein the at least one processor is further programmed to act as aspoken dialog hypothesis generator that generates at least one of theplurality of spoken dialog hypotheses and a confidence value for aninput from a user.
 14. The system of claim 13, wherein the at least oneprocessor is further programmed to act as a comparator that comparessaid confidence value to said threshold.
 15. The system of claim 14,wherein said comparator outputs said spoken dialog hypothesis as anaccepted spoken dialog hypothesis if said confidence value exceeds saidthreshold.
 16. The system of claim 12, wherein said threshold assignerassigns said threshold based upon the classification by said spokendialog hypothesis classifier.
 17. The system of claim 12, wherein saidspoken dialog hypothesis classifier classifies said plurality of spokendialog hypotheses based upon a frequency of occurrence of said spokendialog hypotheses.
 18. The system of claim 17, wherein said thresholdassigner determines a smaller value for the first threshold as thefrequency of occurrence of one or more hypotheses in the first classincreases.
 19. The system of claim 17, wherein said threshold assignerdetermines a larger value for the first threshold as the frequency ofoccurrence of one or more hypotheses in the first class decreases.
 20. Asystem for recognizing speech input provided by a user, the systemcomprising: at least one processor programmed to: for each class of aplurality of classes of spoken dialog hypotheses, determine a thresholdfor use in evaluating a confidence level of the semantic interpreter inan output of the semantic interpreter that comprises one of the one ormore hypotheses in the class, each of the plurality of spoken dialoghypotheses being a potential semantic interpretation that a semanticinterpreter is able to output as a result of a semantic interpretationprocess performed on an input, wherein the determining comprisesdetermining a first threshold for a first class of spoken dialoghypotheses and a second threshold for a second class of spoken dialoghypotheses, the first threshold having a different value than the secondthreshold, the first class comprising at least one first spoken dialoghypothesis and the second class comprising at least one second spokendialog hypothesis, the at least one first spoken dialog hypothesis beingdifferent from the at least one second spoken dialog hypothesis, whereinthe determining comprises adjusting the first threshold based at leastin part on a number of times the at least one first spoken dialoghypothesis was previously output by the semantic interpreter; for afirst input received from the user, semantically interpret the firstinput to determine a hypothesis for the first input, from the pluralityof spoken dialog hypotheses, and a confidence value; determine a classof the plurality of classes of spoken dialog hypotheses into which thehypothesis is classified and, based on the class, at least one thresholdassociated with the class for use in evaluating a confidence level of ahypothesis; and compare the confidence value for the hypothesis to theat least one threshold associated with the class of the hypothesis. 21.The system of claim 20, wherein the at least one threshold in the setsof thresholds associated with the class comprises an acceptancethreshold and a rejection threshold, and wherein the at least oneprocessor is programmed to compare the confidence value to the at leastone threshold by: accepting the speech recognition hypothesis when theconfidence value exceeds the acceptance threshold; rejecting the speechrecognition hypothesis when the confidence value does not exceed therejection threshold; and prompting the user regarding the spoken dialoghypothesis when the confidence value exceeds the rejection threshold butdoes not exceed the confirmation threshold.